GGE BIPLOT ANALYSIS FOR GENOTYPE X ENVIRONMENT INTERACTION ON YIELD TRAIT OF HIGH FE CONTENT RICE GENOTYPES IN INDONESIAN IRRIGATED ENVIRONMENTS

Abstract: The objective of this work was to select superior sugarcane (Saccharum officinarum) clones with good stability and adaptability, considering the genotype x environment interaction in two productive cycles. Twenty-five early clones plus five control clones were evaluated during two cuts (ratoon cane and plant cane) in 24 environments. A randomized complete block design was used, with three replicates. Tons of stems per hectare and tons of pol per hectare were evaluated. To verify adaptability and stability, the bisegmented regression and the multivariate (AMMI and GGE biplot) methods were used. According to the three methods, which are complementary regarding the desired information, the most promising clones in terms of stability and general adaptability are G5, G12, and G13; the last two are closest to the ideal genotype. The G13 clone is highly productive in favorable and unfavorable environments, presenting the highest averages for ton of stems and pol per hectare. The G3, G4, G10, G15, G17, G18, G22, G23, G25, G26, and G30 clones are not recommended for the 24 evaluated environments.

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Genotype X Environment Interaction by AMMI and GGE-Biplot Stability Analysis in Grain Yield for Soybean [(Glycine Max L.) Merrill] in Ethiopia

International Journal of Forestry and Horticulture ◽

10.20431/2454-9487.0504002 ◽

2019 ◽

Vol 5 (4) ◽

Keyword(s):

Glycine Max ◽

Stability Analysis ◽

Grain Yield ◽

Genotype X Environment Interaction ◽

Environment Interaction ◽

Gge Biplot ◽

Genotype X Environment ◽

Glycine Max L

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GGE-Biplot analysis of multienvironments yield trials of Egyptian cotton (Gossypium barbadense L.,)

International Journal of Cotton Research and Technology ◽

10.33865/ijcrt.002.0403 ◽

2021 ◽

Vol 2 (1) ◽

pp. 61

Author(s):

Ahmed Mohamed Abdelmoghny ◽

Reham Helmy Gibely ◽

Mariz Sobhy Max ◽

Emad Abdelazeim Amer ◽

Salah Saber Hassan

Keyword(s):

Block Design ◽

Environment Interaction ◽

Gge Biplot ◽

Cotton Yield ◽

Comparative Performance ◽

Genotype X Environment ◽

Biplot Analysis ◽

Cotton Genotypes ◽

Egyptian Cotton ◽

The Ideal

GGE biplot technique is one of the most appropriate methods for investigating the genotype x environment interaction. A total of twenty one Egyptian cotton genotypes were tested to evaluate stability and adaptability during two agricultural years at four environments using randomized complete block design with six replications. The analysis of variance showed that the effect of environments, years, environment x years, genotypes, and genotype x environment was highly significant for lint cotton yield / plot. Also, the interaction effect due to G x Y x E was also significant. The variation of sum of squares was divided for genotypes, years, environments, and GEI to 25.261 %, 0.574 %, 36.660 % and 3.396 % respectively of total variance for lint cotton yield / plot. The analysis of environments revealed that the cotton genotypes showed maximum mean values for lint cotton yield in El-Gharbiya then Kafr El-Sheikh. Comparative performance of genotypes through genotype by environment interaction (GEI) revealed that genotypes produced maximum lint cotton yield during 2019 at El-Gharbiya followed by Kafr El-Sheikh. The results of biplot analysis showed that the first and second principle components accounted 87.96 % and 5.86 %, respectively, and in total of 93.82 % lint cotton yield variance. The polygon view led to the identification of top six genotypes. G6, G7 and G15 were the ideal genotypes which has the highest mean performance coupled with maximum stability. The ideal genotype could be used as a benchmark for selection. While, the desirable genotypes was (G2, G4 and G5) characterized by high mean yield but less ideal genotypes. E2 was the ideal environment across four environments which have the highest ability to discriminate the genotypes. Four environments had long vectors with small angles (acute) are highly correlated and clustered as one mega-environment. The cotton breeder should evaluate the genotypes under new environments to reduce the costs.

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